Image coding is getting significant for current applications, for instance, HDTV, multimedia, videophone, video conference and video storage. To remove possible redundancy in the related data is imperative since there are numerous data to be processed. The most practical method to meet the demand is data compression. Spatial and/or temporal redundancy is eliminated to accommodate communication or storage. The common techniques for data compression are predictive coding, transform coding, vector quantization (VQ), visual pattern image coding (VPIC), and block truncation coding (BTC), etc.
BTC [Edward J. Delp and O. Robert Mitchell, IEEE Transactions On Communications, Vol. Com-27, No. 9, September 1979] is an efficient method for block correlated signals. It is a moment preserving quantizer, that is, it quantizes block data into two values by preserving the first moment and the second moment. The advantages of BTC are simple computation, easy implementation and fine edge preservation, etc. The main problem of BTC is its high bit rate. In a fixed block size BTC, the bit rate of BTC is about 1.625 bits per pixel. There are some algorithms which can obtain a lower bit rate and fewer errors. However, the computation is so complex that it is hard to be implemented in VLSI design and in real-time processing.
VPIC is a high quality algorithm introduced by Dapang Chen and Alan C. Bovik [IEEE Transactions On Communications, Vol. 38, No. 12, December 1990]. Unlike other algorithms, VPIC uses pixel values of a block to compute block gradient orientation. Then the algorithm selects the bilevel block pattern with a simple viewing geometry model. Pixels are quantized into two levels by the block mean, gradient magnitude, the gradient orientation, and the predefined block pattern. High compression ratio is a marked advantage of VPIC. Nevertheless, if a block does not contain an obvious edge, VPIC will mismatch the block pattern. It will make serious error because of this mismatching.
Images are often corrupted because of the impulse noise caused by image capturing devices. Before being encoded, an image is often preprocessed by median filter. There are two kinds of filter technology: The first method processes every pixel of an image by filter no matter whether the image is with or without noise. The second method detects noise and then processes the image with filter technology. The high computation of the first method is remarkable; however, its distortion is gross. The second method dose not distort image much but it spends much time in image processing. As a result, it is imperative and significant to find an efficient algorithm to remove noise.
In terms of up-to-date image coding systems such as JPEG, an image is preprocessed so that the impulse noise is removed. Nevertheless, not all of the images are preprocessed by filter. Some of the reconstructed images will thus be inferior in quality. To combine preprocess and source coding may become the best solution possible to the problem.